There is no going back: The case for starting over with conditional sentences
Bibliographic record
Abstract
The life of conditional sentences of imprisonment in Canada has been, to say the least, turbulent. Introduced in 1996, it was not long before restrictions were placed on their use—first in 2007 and then again in 2012. To add insult to injury, the sanction was found to have essentially failed in meeting its primary objective (of prison reduction) in two studies released in 2019. In many people’s minds, this less-than-stellar performance as a prison alternative signaled the inevitable end to this sentencing option. Yet, despite the many challenges, recent (2021) developments suggest that predictions of its imminent death may have been premature. Indeed, a resurrection of sorts may be on the horizon, brought about either through jurisprudence (Sharma) or legislation (Bill C-22). Having said this, any hope of long-term salvation will require serious analysis of its failings and deep reflection of workable remedies. This study proposes to carry out this task. To this end, it employs a mixed-methods design (quantitative court and survey data as well as qualitative interviews with judges) to explore the use of conditional sentences in British Columbia, the province that appears to have had the least success in terms of using the sanction as a true prison alternative. The many challenges of conditional sentencing (e.g., flawed statutory construction, lack of public education, inadequate funding, etc.), are highlighted through a thematic analysis of the data. The phenomena of net-widening and circumvention are each explored as possible explanations for the apparent stability of imprisonment rates over the decades, notwithstanding dramatic swings in Canadian penal policy. Most notably, the application of conditional sentences to offenders who would not otherwise have been facing jail is linked to a rejection of the sanction as a term of imprisonment and/or its appeal as a form of “robust probation.” The future of conditional sentencing in Canada is considered and an argument is made that simply removing the restrictions introduced in 2012 fails to acknowledge or address the sanction’s many flaws. Indeed, if the challenges of conditional sentencing are not resolved, there may be little reason to believe that the sanction will fare any better than it did in its earlier (pre-2007/2012) life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".